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SAJEMS NS 15 (2012) No 2
MACROECONOMIC IMPACT OF ESKOM’S SIX-YEAR CAPITAL
INVESTMENT PROGRAMME1
Reyno Seymore
Department of Economics, University of Pretoria
Olusegun A. Akanbi
Department of Economics, University of South Africa
Iraj Abedian
GIBS, University of Pretoria
Accepted: February 2012
Abstract
This study analyses the impact of an increase in Eskom’s capital expenditure on the overall macro and
sectoral economy using both a Time-Series Macro-Econometric (TSME) model and a Computable General
Equilibrium (CGE) model. The simulation results from the TSME model reveal that in the long run, major
macro variables (i.e. household consumption, GDP, and employment) will be positively affected by the
increased investment. A weak transmission mechanism of the shock on the macro and sectoral economy is
detected both in the short run and long run due to the relatively small share of electricity investment in total
investment in the economy. On the other hand, the simulation results from the CGE reveal similar but more
robust positive impacts on the macro economy. Most of the short-run macroeconomic impacts are
reinforced in the long run.
Key words: capital expenditure, macroeconomic variables, general equilibrium modelling
JEL: C01, 32, 51-54, D58
1
Introduction
Over the period 1994-2010 the South African
economy sustained itself with the existing
energy infrastructure built before 1994 without
investing in additional capacity to meet the
growing demands of the economy. This development has created a huge constraint on the
growth prospects of the country. However, the
need to increase energy capacity in the country
became very urgent fifteen years postdemocracy. The state-owned utility company,
Eskom, has drawn up a six-year capital
investment programme to increase the energy
capacity of the country.
In this study, the impact of Eskom’s sixyear capital expenditure on the macro and
sectoral economy is explored. Two basic
approaches are used in the analysis that follows,
namely a Time-Series Macro-Econometric
(TSME) model, and a Computable General
Equilibrium (CGE) model.
The TSME model quantifies the impacts of
the Eskom capital expenditure on the economy
under a dynamic framework while the CGE
model seeks to examine this in a static
framework. The static framework (CGE) in
this instance may not be able to capture the
structural changes that occurred over the years
in the economy. On the other hand, the
dynamic framework (TSME) may also not be
able to capture the impact on the sub-industries
component of the economy. In addition, the
CGE model takes into consideration the micro
implications of any shocks in the system while
this is not so explicit in the TSME. These two
approaches are expected to complement each
other. However, the complementarity of these
models is expected to serve as a robustness
check for the simulation results.
SAJEMS NS 15 (2012) No 2
143
It is important to note that the econometric
analysis presented in this study represents a
comparative static analysis. This means that
the models only take into consideration one
particular shock (capital investment) to the
system while every other thing remains the
same. Therefore, the magnitude and direction
of the response variables could have been
cushioned by other shocks (monetary and
fiscal shocks) in the system.
In both the short run and the long run,
capital stock/gross investment in the electricity
sector is exogenously increased by 18 per cent.
In detecting the 18 per cent shock, a base year
was set at 2010 when Eskom’s total asset value
was R246 135 million (Eskom, 2010). Therefore,
the six-year capital expenditure programme
after adjusting for inflation (6 per cent) and
depreciation rate (10 per cent) is cumulatively
added to the asset value leading to an 18 per
cent average real growth.
The impact of the 18 per cent shock is less
in the TSME model, due to its dynamic nature.
The share of capital investment in the
electricity sector to total capital investment in
the economy is very insignificant over time.
Therefore, an 18 per cent shock in this sector
will not have a significant impact on major
macro variables.
The rest of the study is structured as
follows: Section Two provides a description of
the cost of supplying sustainable energy in the
South African context; Section Three provides
the TSME and CGE models simulations design
as well as an analysis of the capital expenditure
project; Section Four provides an overall
conclusion.
2
Cost of supplying a
sustainable energy
It is stating the obvious that to provide a
sustainable supply of energy more capital
investment is needed. For the period up to
2009, the South African economy had been
sustaining itself with the stock of energy
infrastructure built before 1994. Thereafter not
much investment was done in the building of
more capacity that would cater for the growing
demand, arising from higher economic growth
and/or the developmental needs of a modern
economy. The failure to invest adequately has
created a real constraint to the growth
prospects of the country. However, the need to
increase energy capacity in the country has
become self-evident. In response, Eskom has
drawn up a six-year capital investment
programme that will increase energy capacity
in the country to levels in line with the
expected rise in national electricity demand
(Table 1).
Table 1
Eskom capital expenditure programme
Capital expenditure (excl IDC
and cost of cover)
FY10/11
FY11/12
FY12/13
FY13/14
FY14/15
FY15/16
FY16/17
R'm
Generation
63,249
69,563
53,585
34,955
49,119
77,028
89,827
Transmission
14,533
11,327
16,112
16,667
22,099
23,592
24,392
8,654
10,705
12,784
14,893
17,129
20,129
Distribution
Corporate (incl ED)
679
Research and development
851
372
423
317
366
529
474
22,187
282
2,706
3,628
2,275
5,630
2,332
TOTAL
87,967
94,673
86,533
69,107
89,241
126,853
139,019
Cumulative
87,967
182,639
269,172
338,279
427,520
554,373
693,392
Source: Eskom
Note: These figures are preliminary estimates and are not necessarily the actual figures used by Eskom’s final capex
programme.
In the process of achieving a sustainable
supply and distribution of energy in South
Africa, Eskom will need to finance its capital
requirements, generating more revenue in order
to be able to finance its required capital
expenditure. The options available in financing
the required capex include:
1) User charges, increases in electricity prices.
144
2) Government capitalisation of Eskom.
3) Private sector investment.
4) A mix of all of the above.
Each of these options has its respective pros
and cons.
SAJEMS NS 15 (2012) No 2
Government capitalisation of Eskom
This option has its own systemic implications.
At face value, if government capitalises
Eskom, it reduces the need for raising user
charges. So in the short term, the economy
operates along its business as usual trajectory.
However, there are implications for this
scenario over the medium to long term. Most
importantly, the following issues arise:
I Government capitalisation of Eskom entails
either tax increases or a rise in government
debt. Both these developments have a
medium- to long-term distortion impact on
the stability of the economy. Whilst the
arguments here are complex and interrelated,
large scale capitalisation of Eskom will
constrain the government’s ability in financing
key requirements in socio-economic infrastructure.
II This approach also delays energy charges
becoming cost-reflective, and as such it is
not favourable for the long-term efficient
utilisation of the country’s resources. Nor is
it helpful in promoting the economy’s
dynamic global competitiveness.
III Government’s adequate capitalisation of
Eskom, if it leads to the subsidisation of
electricity charges, will also entail social
welfare implications. It is likely that it will
exacerbate the already highly skew distribution
of income in the country.
Increasing user charges
Financing Eskom’s capex requires considerable
increases in electricity charges if they are to
earn an adequate return on their investment but
the welfare effect may be negative (Collier,
1984:30). Sudden and substantial increases,
however, lead to disruptions for many business
firms that had not anticipated such sharp
increases. Furthermore, business operations that
are operating at the margins of profitability
cannot absorb substantial cost increases, be it
electricity or other costs. A good case in point
is the marginal gold mining industries, or some
of the manufacturing firms that are hard-hit by
a mix of currency appreciation and unfavourable
global economic conditions. For such firms, it
is not the increase per se that matters, rather it
is the quantum of increases in the short term
that leaves them with little or no degrees of
freedom to absorb the production cost
increases. In such cases, business firms have
no option but to scale down operations, lay off
workers and in some extreme cases even close
down.
Increasing user charges however has distinct
benefits too. Key amongst them are the
Private-sector investment
following:
Whilst this option is clearly part of the
a It creates a platform for sustainable and
medium- to long-term solution, it is constrained
reliable electricity generation in the country.
by the following factors:
b It helps set electricity prices at costI
There is no guarantee that private-sector
reflective levels. In the long term, this is
investment would entail lower increases in
a pre-condition for an efficient allocation
user charges. In fact, the contrary might be
of resources within the economy. The
true, at least in the short term.
country’s dynamic global competitiveness
requires that we ensure all resources used II It is safe to argue that in the short term
the regulatory framework for private-sector
are as cost-reflective as possible.
participation is not in place. As such, this
c Economic stability over the long term
option remains a viable one over the medium
necessitates sustainable use of all resources,
to long term.
electricity included.
d Cost-reflectivity will also open up oppor- III In general, private-sector investment should
be encouraged with a view to diversifying
tunities for alternative energy options. This
the sources of national energy supply. In the
in turn will diversify sources of energy in
process, care should be taken that costthe country, and lead to further stability
reflectivity is not compromised and market
arising for diversification.
contestability is encouraged.
SAJEMS NS 15 (2012) No 2
A mix of all options
In reality, Eskom’s investment programme is
partly financed via National Treasury’s
capitalisation and partly with the help of usercharge increases. Whilst politically this might
be inevitable, it is important that the key
elements of long-term sustainability are not
undermined in the process.
3
Empirical analysis
3.1 Simulation results: time-series
macro-econometric (TSME)
approach
This approach looks at the short- and long-run
impacts of an increase in Eskom’s capital
expenditure on the economy in a dynamic
system. It takes into consideration the dynamic
adjustment processes and provides a contemporaneous feedback of any shock to the entire
system. A more detailed explanation of the
model specification and closures, data and
methodology can be seen in Appendix 1.
The analysis presented in this study reveals
the impact of a shock in a particular variable
(capital investment) on the entire macro
economy. The estimated coefficients in each of
the behavioural equations (as presented in
Appendix 1) represent an average value over
the period covered in the macro model which
has captured the entire dynamic features
embedded in the system. There is a high
expectation that these dynamic features will
reoccur in future. In other words, it is assumed
that there will be no major change in the
structure of the economy in the short- to
medium-term period that will cause a huge
change in the magnitude of the estimated
coefficients. The estimated coefficients, however,
show both the short-run and long-run path of
the economy and, therefore, a shock on a
particular variable will be reflected through
them. Given the trend path of the economy, the
impact of any present and future shocks to the
system will be captured.
In order to capture the impact of Eskom’s
capital expenditure on the entire economy,
fixed investment in the electricity, gas and
water sector is assumed to be exogenous in the
model – over 80 per cent of fixed investment
145
in this sector comes from electricity. The idea
being that capital expenditure will translate
into some form of capital stock over time and
given the fact that the current capital stock is
the sum of existing capital stock (taking into
consideration the rate of depreciation) and
current investment.
The short-run and long-run simulations are
tested by shocking fixed investment in
electricity, gas and water sector by 18 per cent
per year over a six-year period. Due to the lag
values included in the short-run dynamic
equations (error correction model), the entire
model simulations covers 1974-2009. However,
the six-year (18 per cent per year) dynamic
shock on fixed investment was applied from
1974 through to 1979 while its impact on the
macro system filtered through the entire period
covered in the model2. All the results are
reported as percentage changes (elasticity)
from the baseline scenario. In other words, the
results are not forecasts of various economic
variables, but rather deviations from its shortand long-run path due to increases in the
capital expenditure.
The elasticities are computed by comparing
every response variable’s baseline simulation
path with its shocked simulation path. Elasticity
is defined as the percentage change in the
response variable relative to the percentage of
the shock applied. The dynamic elasticities are
determined along the simulation path, whereas
elasticities at convergence are the long-run
elasticity (Klein, 1982:135).
Given the small sample size it is difficult to
ensure convergence within the sample. To
facilitate the detection of convergence, HodrickPrescott (HP) filters were applied and the
smoothed dynamic elasticities were graphed.
However, the first value of the HP filter
represents the short-run impact and the last
value represents the long-run impact (Akanbi
& Du Toit, 2011; Du Toit, 1999). The time
paths of elasticities of the major response
variables for a particular shock are presented in
Appendix 1.
To test the stability of the macro-econometric
model and whether the behavioural equations
estimated are robust, the actual and fitted
(estimated) series of the major macro variables
are plotted in Appendix 2. Close to a perfect fit
was detected, suggesting that the estimated
146
SAJEMS NS 15 (2012) No 2
coefficients in the model are robust and accurate
for predicting the impact of any policy actions3.
Economy-wide econometric sensitivity
analysis
In this section, the short-run and long-run
impact of an increase in capital expenditure by
Eskom on some major macro variables in the
economy is analysed. As mentioned earlier, the
explicit assumption made in this study is the
exogenous nature of fixed investment in the
electricity, gas and water sector.
Table 2 presents the short- and long-run
impact of an increase in capital expenditure
by18 per cent per year over a six-year period.
As expected, the impacts are positive on the
major macroeconomic variables except for some
short- run impacts, which could be attributed to
the slow adjustment processes embedded in the
system. The positive impact of an increase in
capex was found to be less significant than the
negative impacts of an electricity price increase4.
The reason for this is that the price increases
have a direct impact on inflation and serve as a
linkage between the demand side and supply
side of the economy. On the other hand, the
capex increase has an indirect impact on inflation
via excess demand (difference between domestic
expenditure and production) in the economy.
Table 2
Economy-wide impacts of an increase in Eskom’s capital expenditure
Long run
Short run
Employment
0.01%
0.002%
Output
0.07%
0.01%
Investment
0.06%
1.8%
Household consumption
0.09%
0.004%
Real wages
0.05%
0.04%
Exports
0%
0.007%
Imports
0.07%
0.01%
Exchange rate (R/$)
-0.18%
-0.12%
Consumer inflation
-0.1%
-0.11%
Source: Author’s calculations
Note: The long-run values are depicted using the Hodrick-Prescott trend value. Negative values represent a decline
while positive values represent an increase.
In the long run, total investment will rise by
about 0.06 per cent leading to an output (GDP)
increase of about 0.07 per cent as a result of an
18 per cent capex increase over a six-year
period. Due to this, employment and real wages
will increase by about 0.01 per cent and 0.05
per cent respectively. Consumer inflation will
deviate from its long-run path by about 0.1 per
cent. This is attributed to the much higher
impact on GDP than domestic expenditure,
which resulted in a declining excess demand.
As inflation shrinks and GDP rises, the
exchange rate (rand) will appreciate by about
0.18 per cent in the long run. Given the net
impact of falling inflation and rand appreciation
the long-run exports will remain unchanged
while imports will rise by about 0.07 per cent
due to output increases.
The short-run impacts on total investment
will be more robust at about 1.8 per cent due to
the direct capex injection. The impact on the
exchange rate and inflation will also be positive
and in line with its long-run path.
Sectoral econometric sensitivity analysis
The response of major sectoral variables to the
shock in Eskom’s capital expenditure will
depend on the relative share of inputs (labour
and capital) used in the production process.
Table 3 presents the result of an 18 per cent
increase in capital expenditure on sectoral
variables over a six-year period.
Given the indirect linkage between investment
in the electricity sector and investment in the
other sectors, the impact of the shock on other
major variables was not strong. In other words,
the impact of the shock on other sectors is felt
through aggregate demand and supply. Sectors
that tend to response stronger in the long run in
terms of investment are also those that have
SAJEMS NS 15 (2012) No 2
147
the highest energy intensity. The financial
service and construction sector investment will
in the long run increase by only about 0.05 per
cent and 0.03 per cent, respectively. These are
the sectors with the lowest energy intensity.
Table 3
Sectoral impacts of an increase in Eskom’s capital expenditure
Long-run
Employment
Exports
Imports
0.01%
0.07%
0.08%
0.07%
-0.01%
0.08%
-0.004%
0.03%
0.15%
0.02%
0.03%
0.07%
Agricultural sector
0.004%
0.07%
0.12%
0.06%
0.01%
0.05%
Construction sector
0.002%
0.001%
0.03%
0%
Electricity, gas & water sector
1.5%
1.8%
1.5%
-0.2%
6%
Wholesale & retail trade sector
0.02%
0.05%
0.08%
0.04%
-0.01%
0.04%
Transport & communication
sector
-0.003%
0.04%
0.13%
0.03%
Financial service sector
-0.01%
0.03%
0.05%
0.03%
0.001%
Manufacturing sector
Mining sector
Output
Investment
Real wages
0%
-0.03%
0.005%
0.03%
Short-run
Manufacturing sector
0.01%
0.05%
Mining sector
-0.1%
0.006%
-0.14%
-0.07%
0.2%
-0.01%
Agricultural sector
-0.04%
-0.09%
-0.4%
-0.02%
-0.02%
-0.01%
0.02%
0.05%
-0.1%
0.06%
Construction sector
0%
0%
0%
Electricity, gas & water sector
0.14%
0.1%
0.16%
0.5%
Wholesale & retail trade sector
0.03%
0.06%
0.3%
0.05%
0.02%
0.02%
Transport & communication
sector
0.16%
0.07%
0.08%
0.2%
0.01%
-0.01%
Financial service sector
0%
0%
0.02%
0%
-0.02%
0.05%
-0.3%
Source: Author’s calculations
The long-run values are depicted using the Hodrick-Prescott trend value. Negative values represent a decline while positive
values represent an increase.
Based on the above scenario, the long-run
output in the electricity sector will increase by
about 1.8 per cent due to the direct impact of
the investment shock. The impact on output in
the different sectors of the economy will
indirectly feed in through falling inflation as
excess demand shrinks. Therefore, the price
block (inflation) serves as a linkage between
the various sectors in the economy and the
sensitivity on inflation from each sector will
partly be reflected in output. Output in the
agricultural and manufacturing sectors will
increase by about 0.07 per cent each in the
long run. Output responses in other sectors of
the economy will not be economically
significant with the construction sector posing
almost no impact in the long run.
Employment and real wages in the
electricity sector will rise by about 1.5 per cent
each in the long run. Given the small impact on
output in other sectors, long-run employment
change will be negligible. The impact of real
wages will follow the direction of output as
increased output leads to increased productivity.
Exports will decline in the sectors where the
effect of the exchange rate is stronger than that
of inflation. Although the impacts are relatively
insignificant, exports in the manufacturing,
wholesale and retail and financial sectors will
decline in the long run by about 0.01 per cent
and 0.03 per cent, respectively. On the other
hand, imports will be much more driven by the
trend in output. The negative impact of the
shock on employment in some sectors (i.e.
mining, transport & communication and financial
services) reflects the larger negative effect
of higher real wages in their employment
function.
The short-run implications also remain
negligible and, due to the slow adjustment
process embedded in the system, the impacts
may not be easily visible. The response of the
148
shock in the mining and agricultural sector was
negative across the board while no change in
output, employment and real wages will be
recorded in the construction and financial
sectors.
3.2 Simulation results: computable
general equilibrium (CGE)
approach
This section looks at the short- and long-run
impacts of the capital expenditure programme
on the economy in a static system. The
UPGEM model for South Africa is used in
these simulations, and is formulated and solved
using GEMPACK, a flexible system for solving
computable general equilibrium (CGE) models.
The UPGEM model is designed for comparativestatic analysis of policy issues and is similar to
the ORANI-G model of the Australian economy,
which is fully presented and explained by
Horridge (2000).
A typical short-run closure is applied where
the rate of return on capital is allowed to
change while the capital stock is fixed. Furthermore, the supply of land is fixed. Aggregate
investment, inventories and government consumption are exogenous, but the trade balance
and consumption are endogenous. Also, rigidities
in the labour market are allowed for by holding
real wages fixed. The length of the short run is
not explicit, but usually seen as between 1 and
3 years (Horridge, 2000).
In the long run, fixed rates of return are
maintained, while capital stocks are free to
adjust. The exception is capital stocks in the
electricity sector, which are exogenously
increased. There is no link between domestic
saving and capital formation and an open
capital market is therefore implicitly assumed.
Also, real skilled wages adjust, while skilled
employment is fixed. In other words, the rate
of skilled unemployment and the labour force
of skilled labour are in the long run determined
by mechanisms outside of the model.
However, unskilled labour is allowed to vary
in the long run due to the high structural
unemployment of unskilled workers in South
Africa. Also, aggregate real government demand
is seen as determined by mechanisms outside
of the model.
In both the short run and the long run,
capital stock in the electricity sector is
SAJEMS NS 15 (2012) No 2
exogenously increased by 18 per cent. The
impacts of capital expenditure in the electricity
sector on the macro and sectoral economy are
considered. As was the case in the previous
section, all the results are reported as percenttage point changes from the base scenario.
In other words, the results are not forecasts
of various economic variables, but rather
deviations due to increases in the capital
expenditure.
Economy-wide computable general
equilibrium analysis
Table 4 shows the short-run and long-run
macroeconomic impact of capital expenditure
increases on the South African economy. In
the short run, increased capital expenditure
will result in a real devaluation of the currency.
Increased capital expenditure will increase the
demand for imports, especially inputs used in
the capital-expenditure process. This increased
demand will result in an increased supply of
rand, weakening the real value of the currency.
However, in the long run, the increased capital
expenditure is expected to increase the productive capacity of the country, improving the
competitiveness of industries in South Africa,
and therefore in the long run an appreciation of
the currency is expected.
The terms of trade, which is the ratio
between export prices and import prices, will
weaken in the short run as well as in the long
run. The real devaluation in the short run will
lead to an increase in the domestic currency
received for exports. This will, for exporting
industries, counter the price impacts of
increased capital expenditure. However, the
real devaluation will increase the price of
imports, in terms of the domestic currency, and
therefore the price of imported inputs in the
production process, as well as the price of
imported household products. The net impact
will be that relative import prices will increase
more than the relative increase in export
prices. In the long run, the real appreciation of
the currency, as well as higher real household
consumption, will increase the demand for
imports. Also, the real appreciation will
decrease the rand value received for exports,
and the net impact will be a weakening in the
terms of trade.
SAJEMS NS 15 (2012) No 2
149
Table 4
Economy-wide impacts of an increase in electricity prices
Long-run
18 per cent
Real devaluation
-0.18
Terms of trade
-0.13
Gross operating surplus
0.56
Real household consumption
1.22
Aggregate capital stock
0.52
Real GDP
0.75
Average real wage
1.05
Unskilled employment
0.79
Short-run
18 per cent
Real devaluation
Terms of trade
0.13
-0.14
Gross operating surplus
0.39
Real household consumption
0.46
Aggregate real government demands
0.45
Real GDP
0.45
Skilled employment
0.40
Unskilled employment
Source: Author’s calculations
The increase in capital expenditure will, in the
short run (0.39 per cent) and in the long run
(0.56 per cent) increase gross operating surplus.
Capital is variable in the long run, thus the
impact on the gross operating surplus is
somewhat strengthened due to the ability to
move capital to higher yielding industries, or
by increasing investment. Real household
consumption will increase by 0.46 per cent in
the short run, mainly due to the positive
employment (number of workers) impacts. As
industries increase their output (Table 3.5),
more workers will be employed. In the long
run, skilled labour is exogenous, but skilled
wages are allowed to adjust. This will then
reinforce the real household consumption
increase in the long run (1.22 per cent) as
unskilled employment (the number of workers)
as well as skilled wages increase.
Because the gross operating surplus and real
household consumption will increase, government revenue will also increase in the short
run. If we assume a neutral government budget,
real government demands will also increase.
For an 18 per cent increase in capital stock in
the electricity sector, aggregate net capital
stock in the economy is expected to increase
0.55
by 0.52 per cent in the long run.
The impact on real GDP should be positive,
since consumer spending, gross operating
surplus, aggregate capital stock and government
demands are expected to increase. The impact
of a capital expenditure increase of 18 per cent
on the real gross domestic product (GDP) will
be an increase of about 0.45 per cent in the
short run and 0.75 per cent in the long run.
Sectoral computable general
equilibrium analysis
All sectors use electricity, directly or indirectly,
as an input, therefore increased capital expenditure in the electricity sector, leading to
increased capacity to produce electricity, will
have an impact on all the industries of the
economy. Furthermore, various industries will
be directly affected through an increased
demand for inputs used in capital expenditure
as well as increased demand for the inputs
used to produce electricity. It is therefore
expected that some structural changes will take
place in the economy. (Complete descriptions
of industries and detailed results are provided
in Appendix 3). Due to the multi-industry
disaggregation in the CGE approach, the
150
SAJEMS NS 15 (2012) No 2
effects of the capital expenditure are presented
according to variable changes by sector.
Output (GDP) impacts
Table 5 provides an impact range, with an
upper- and a lower- bound impact, based on
sub-industry impacts, of the capital expenditure
increases in the nine Standard Industrial
Classification (SIC) sectors. Disaggregated 39
sub-industry output changes are provided in
Appendix 3.
Table 5
Sectoral output impacts
Long-run
Impact range
18 per cent
Agriculture, hunting, forestry and fishing
Upper
Lower
0.75
0.19
Mining and quarrying
Upper
Lower
1.82
0
Manufacturing
Upper
Lower
4.07
-0.25
Electricity, gas and water supply
Upper
Lower
5.88
0.87
Construction
0.15
Wholesale and retail trade; repair of motor vehicles, motor cycles and personal
and household goods; catering and accommodation
Upper
Lower
0.89
0.35
Transport, storage and communication
Upper
Lower
1.11
0.82
Financial intermediation, insurance, real-estate and business services
Upper
Lower
0.96
0.56
Community, social and personal services
Upper
Lower
1.42
0.62
Short-run
Impact range
18 per cent
Agriculture, hunting, forestry and fishing
Upper
Lower
0.21
0.09
Mining and quarrying
Upper
Lower
1.34
0.1
Manufacturing
Upper
Lower
2.6
0.1
Electricity, gas and water supply
Upper
Lower
5.65
0.4
Construction
0.12
Wholesale and retail trade; repair of motor vehicles, motor cycles and personal
and household goods; catering and accommodation
Upper
Lower
0.41
0.18
Transport, storage and communication
Upper
Lower
0.34
0.25
Financial intermediation, insurance, real-estate and business services
Upper
Lower
0.29
0.06
Community, social and personal services
Upper
Lower
0.45
0.38
Source: Author’s calculations
In the short run, all industries are expected to
increase output as a result of the increased
capital expenditure in the electricity sector. In
the Agriculture, Hunting, Forestry and Fishing
sector, the impact will be positive, but
marginal, with output increasing between 0.09
per cent and 0.21 per cent. In the Mining and
quarrying sector, coal output is expected to
increase 0.75 per cent, as the increased output
in electricity increases the demand for coal.
Gold production, an electricity-intensive industry,
is expected to increase by 1.34 per cent as
electricity supply is increased. In the Manufacturing sector, labour-intensive industries are
SAJEMS NS 15 (2012) No 2
expected to increase output marginally between
0.1 per cent and 0.3 per cent. However,
electricity-intensive industries will increase
output up to 2.6 per cent (Iron and Steel). Also,
the increased demand for iron and steel as a
direct result of the capital expenditure programme will increase the profitability of this
industry. The increase in capital expenditure is
expected to increase electricity sold by 5.65
per cent ceteris paribus, while construction is
bound to benefit directly from the expenditure
and increase output in the short run by 0.12 per
cent. This relatively small increase might be an
indication of some crowding out in the
construction sector. The capital expenditure in
the electricity sector could be expected to
increase construction prices, thereby moderating
construction demand in other sectors. General
government spending (0.45 per cent) is
positively affected, given the assumption that
government adjusts spending based on revenue
collected. Revenue collected will be higher due
to the employment impacts (see the next
section), higher economic growth, as well as
higher gross operating profit.
In the long run, output in 37 of the 39
industries is expected to increase if increases in
real capital expenditure are analysed. Also,
short-run output increases are reinforced in the
long run in 31 of the 39 industries. In the long
run, capital stocks are allowed to adjust, and to
move to higher-yielding industries. As electricity
becomes more abundant it could be expected
that capital stocks might flow towards more
electricity-intensive industries. This is evident
as the eight industries that perform better in the
short run than in the long run, are relatively
less electricity-intensive than the other industries
in that specific SIC sector. Other mining
(-0.004 per cent) and Other manufacturing
(-0.25 per cent) will, in the long run, reduce
output. Industries that will increase output in
the long run by less than the increase in output
in the short run are Textiles (0.14 per cent),
Other Non-metallic Mineral products (0.25 per
cent), Other Metal Products (0.34 per cent),
Other Machinery (0.22 per cent), Electrical
Machinery (0.35 per cent) and Transport
Equipment (0.3 per cent). On the other hand,
industries that will increase output by more
than two percentage points in the long run are
Iron and Steel (4.07 per cent), Non-ferrous
151
Metal (3.51 per cent) and the Electricity
industry (5.88 per cent) ceteris paribus.
Employment and wage impacts
Employment impacts will follow the changes
in output, induced by the increase in capital
expenditure in the electricity sector. Industries
will increase output due to increased
availability of electricity, and as a result
employ more workers. In the short run,
industries that will increase employment by
more than 1 per cent for an 18 per cent
increase in capital expenditure are the coal
industry (1.82 per cent), the Gold industry
(2.04 per cent), Iron and Steel industry (5.31
per cent), Non-ferrous Metal industry (3.5 per
cent) and Water industry (2.01 per cent). Coal
is an important input in the production of
electricity, and the increase in electricity
production will increase the demand for coal.
The next three industries are electricityintensive industries which will directly benefit
from increased electricity production, while
water is also an important input in mining and
heavy industries. The increased production in
these industries will increase the demand for
water. The only industry recording a marginal
decrease in employment is the construction
industry (-0.1 per cent). This reflects the
possible crowding-out effect due to higher
construction prices as discussed in the previous
section.
In the long run, skilled employment is fixed
and the change in the number of workers is in
the unskilled categories. In 29 of the 39
industries, the employment impact will be
smaller in the long run than in the short run.
This is due to labour-market differentiation
where skilled labour is fixed and skilled wages
are adjusted upwards. Industries that will
increase unskilled employment in the long run
by a higher percentage than in the short run are
industries that will experience a capital inflow
in the long run, mainly service industries and
consumer goods industries. Two industries will
reduce unskilled employment in the long run,
namely Other Mining and Other Manufacturing, following the reduction in output in
the long run.
The model specifies a Constant Elasticity of
Substitution (CES) relationship between primary
factors. In the model, rates of return (in the
152
SAJEMS NS 15 (2012) No 2
short run) and capital stock (in the long run)
are allowed to vary. CES is seen as an
appropriate functional form in all industries
except the electricity industry. In the electricity
industry, the capital stock is exogenously
increased in the short run and in the long run,
and the model will yield a reduction in
employment in the electricity sector as labour
is replaced by capital. However, it is more
likely that the relationship will follow the
Leontief functional form, where labour will
increase if the use of capital increases. As a
result, employment impacts in the electricity
sector cannot be adequately explained by the
model and the cumulative positive employment
impact presented in this chapter could be seen
as a conservative estimation of employment
gains.
Table 6
Sectoral employment impacts
Long-run
Impact range
18 per cent
Agriculture, hunting, forestry and fishing
Upper
Lower
0.59
0.01
Mining and quarrying
Upper
Lower
2.04
-0.12
Manufacturing
Upper
Lower
3.78
-0.5
Electricity, gas and water supply
Upper
Lower
0.34
n/a
Construction
-0.3
Wholesale and retail trade; repair of motor vehicles, motorcycles and personal
and household goods; catering and accommodation
Upper
Lower
0.57
0.08
Transport, storage and communication
Upper
Lower
0.91
0.65
Financial intermediation, insurance, real-estate and business services
Upper
Lower
0.69
0.37
Community, social and personal services
Upper
Lower
0.8
-0.1
Short-run
Impact range
18 percent
Agriculture, hunting, forestry and fishing
Upper
Lower
0.6
0.35
Mining and quarrying
Upper
Lower
2.04
0.18
Manufacturing
Upper
Lower
5.31
0.2
Electricity, gas and water supply
Upper
Lower
2.01
n/a
Construction
0.2
Wholesale and retail trade; repair of motor vehicles, motor cycles and personal
and household goods; catering and accommodation
Upper
Lower
0.72
0.51
Transport, storage and communication
Upper
Lower
0.7
0.53
Financial intermediation, insurance, real-estate and business services
Upper
Lower
0.38
0.33
Community, social and personal services
Upper
Lower
0.83
0.5
Source: Author’s calculations
In the long run wages are the market clearing
agent for skilled employment. In other words,
the number of skilled workers is fixed. This
reflects the idea that skilled labour is
determined by factors outside the model. On
the other hand, unskilled workers and unskilled
SAJEMS NS 15 (2012) No 2
153
wages are allowed to vary due to the high
unemployment of unskilled workers in South
Africa. It could therefore be expected that the
wage impact due to capital expenditure on
skilled labour would be higher than the impact
on the wages of unskilled labour. This is
reflected in Table 7. Statistics South Africa
(StatsSA), in the national Social Accounting
Matrix (SAM), classifies all occupations into
eleven groups (StatsSA, 2004). This is in
accordance with the South African Standard
Classification of Occupations (SASCO). This
analysis follows the same classification and a
full description of all occupations is available
in StatsSA (2004).
In Table 7 it is shown that skilled workers
will experience real wage increases between
1.2 per cent and 1.6 per cent for an 18 per cent
increase in capital stock in the electricity
sector. Unskilled real wages are expected to
increase by between 0.6 per cent and 0.9 per
cent.
Table 7
Wage impacts by occupation
Long-run
18 per cent
Legislators
1.55
Professionals
1.6
Technicians
1.58
Clerks
1.59
Service workers
1.16
Skilled agricultural workers
1.4
Craft workers
0.9
Plant and machine operators
0.86
Elementary occupations
0.78
Domestic workers
0.68
All Unspecified occupations
0.72
Source: Author’s calculations
The wage impacts by population group are
shown in Table 8. As a larger percentage
of African and Coloured workers are
economically active as unskilled labour, the
positive impact on their wages would be the
smallest (around 1.13 per cent).
Table 8
Wage impacts by population group
Long-run
18 per cent
African
1.13
Coloured
1.12
Indian
1.26
White
1.34
Source: Author’s calculations
On the other hand, as a larger percentage of
white workers are economically active as
skilled workers, the impact on their wages
(1.34 per cent) would be the largest for the
population groups under consideration.
Wage impacts, by population group, are
disaggregated to skilled-wage impacts in Table
9. As discussed, the impacts on skilled labour
across all population groups are larger than the
wage impacts on unskilled labour across all
population groups.
White skilled workers would, for an 18 per
cent increase in capital stock, experience a
1.42 per cent increase in skilled wages, while
154
SAJEMS NS 15 (2012) No 2
Table 9
Skilled wage impacts by population group
Long-run
18 per cent
African
1.28
Coloured
1.33
Indian
1.43
White
1.42
Source: Author’s calculations
African workers would experience a 1.28 per
cent increase. Coloured and Indian workers
would experience a 1.33 per cent and 1.43 per
cent increase, respectively.
Given the fixed long-run skilled employment,
the short-run employment changes by occupation,
as a result of increased capital expenditure, are
shown in Table 10. Employment across all
occupations will be affected positively if
capital expenditure is above the inflation rate.
Table 10
Occupational employment impacts
Long-run
18 per cent
Legislators
0.41
Professionals
0.44
Technicians
0.38
Clerks
0.42
Service workers
0.49
Skilled agricultural workers
0.47
Craft workers
0.24
Plant and machine operators
0.65
Elementary occupations
0.57
Domestic workers
0.49
All Unspecified occupations
0.46
Source: Author’s calculations
All eleven occupational groups will have a
net positive impact on employment, ranging
between 0.24 per cent (Craft workers) and 0.65
per cent (Plant and Machine operators) for an
18 per cent increase. This employment
breakdown is in line with employment and
output production changes by sector. For
example, gold and coal mining will significantly increase output and employment, in line
with the 0.65 per cent increase in plant and
machine operators. On the other hand, the
construction industry will marginally increase
output and marginally decrease employment,
in line with the relatively low increase of 0.24
per cent in craft-worker employment.
Table 11 shows the employment impact of
electricity price increases by population group.
The employment impact will be positive across
all population groups. In the short run,
employment for the African group of workers
will increase the most (0.5 per cent) of all the
population groups.
Approximately 47.5 per cent of African
workers are employed in the Government,
Health and Social Services, Manufacturing and
Mining industries. These industries would
increase output relatively more than the other
industries due to the capital-expenditure
increases in the electricity sector.
SAJEMS NS 15 (2012) No 2
155
Table 11
Employment impacts by population group
Long-run
18 per cent
African
0.5
Coloured
0.41
Indian
0.41
White
0.4
Source: Author’s calculations
4
Conclusion
This study analysed the impact of an increase
in Eskom’s capital expenditure on the overall
macro and sectoral economy using both the
TSME and CGE models.
The simulation results from the TSME
model revealed the macro implications of the
six-year capital expenditure programme. In the
long run, major macro variables (i.e. household
consumption, GDP, and employment) will be
positively affected by the increased investment.
Although the positive impacts of the shock is
very insignificant, due to the relatively small
share of electricity investment in total
investment in the economy. The price block,
which serves as a linkage between the different
segments of the economy, is not directly linked
to investment in electricity. However, a weak
transmission mechanism of the shock on the
macro and sectoral economy is detected both
in the short run and long run.
At the sectoral level, the electricity sector
will boost employment by about 1.5 per cent in
the long run. Impacts on other sectors in terms
of job creation will remain negligible. The
same trend also goes with GDP impact but
with a higher magnitude.
On the other hand, the simulation results
from the CGE revealed similar but more robust
positive impacts on the macro economy.
Overall, in the short run, real gross domestic
product increased, the currency appreciated,
gross operating surplus was higher, aggregate
real household consumption increased, and
skilled as well as unskilled unemployment
increased. Most of the short-run macroeconomic impacts were reinforced in the long
run, with a real appreciation in the currency,
strengthening in the terms of trade, increase in
gross operating surplus, and increase in real
household consumption, as well as a increase
in real gross domestic product, wages and
employment.
Endnotes
1
2
3
4
The study forms part of the final research project commissioned by Eskom (entitled: The Impact of Electricity Price
Increases and Eskom’s Six-Year Capital Investment Programme on the South African Economy) to Pan-African Investment
& Research Services. However, reference to the main document may be mentioned when necessary. The views expressed
in this study are those of author(s) and do not necessarily represent those of Eskom or Eskom policy. The authors gratefully
acknowledge Eskom’s consent to draw heavily on the aforementioned research report for the preparation of this paper.
Since the macro economy has been predicted to follow a particular trend over the years captured through the estimated
coefficients and to detect the dynamic effects over a longer period of time, the shocks were applied from the beginning of
the simulation (Akanbi & Du Toit, 2011; Du Toit, 1999).
The characteristics of the actual underlying data (Stationarity tests) used in the study are presented in the main documents
submitted to Eskom.
Important Note: The analysis presented in this paper is part of the integrated study submitted to Eskom, which includes
separate partial analysis on electricity price hikes. Detailed results of the electricity price hikes impacts, as well as the net
impact of the price hikes and the increases in capex, are presented in the main document submitted to Eskom. The results
revealed that the net impact of an increase in electricity prices and Eskom’s capital expenditure will continue to be negative
on major macro variables (i.e. GDP, employment and investment) in the economy.
156
SAJEMS NS 15 (2012) No 2
References
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Macroeconomic modelling in a changing world: towards a common approach. Chichester: John Wiley and
Sons.
COLLIER, H. 1984. Developing electric power: thirty years of World Bank experience. Washington, D.C:
John Hopkins University Press.
DORNBUSCH, R. 1980. Exchange rate economics: where do we stand? Brookings Papers on Economic
Activity, 1980(1):143-185.
DORNBUSCH, R. 1976. Expectations and exchange rate dynamics. Journal of Political Economy, 84(6):
1161-76.
DU TOIT, C.B. 1999. A supply-side model of the South African economy: critical policy implications.
Unpublished doctoral thesis. Pretoria: University of Pretoria.
DU TOIT, C.B., & MOOLMAN, E. 2004. A neoclassical investment function of the South African economy.
Economic Modelling, 21:647-660.
ENDERS W. 2004. Applied econometric time series (2nd ed.) New York: John Wiley and Sons.
ENGLE, R. & GRANGER, C. 1987. Cointegration and error correction: representation, estimation, and
testing. Econometrica, 55:251-76.
ESKOM. 2010. Annual report 2010. Eskom.
FRANKEL, J.A. 1979. On the mark: a theory of floating exchange rates based on real interest differentials.
American Economic Review, 69(4):610-622.
HORRIDGE, M. 2000. ORANI-G: A generic single-country computable general equilibrium model. CoPS
Working Paper OP-93, Centre of Policy Studies, Monash University.
KLEIN, L.R. 1982. The supply-side of the economy: a view from the prospective of the Wharton model. In
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LAYARD, R. & NICKELL, S. 1986. Unemployment in Britain. Economica, 53:S12-S169.
PRETORIUS, C.J. 1998. Gross fixed investment in the macroeconometric model of the Reserve Bank.
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South Africa.
Appendix 1: TSME model specification, core
structural equations, closure, methodology
and data description
The model captures both the short-run and
long-run dynamic properties of the economy.
As mentioned earlier, four segments of the
economy were captured and include the real
segment, the external segment, the monetary
segment, and the Government (public)
segment. This line of thought has also been
followed in Akanbi and Du Toit (2011).
The real segment consists of aggregate
supply, aggregate demand and the price block.
The aggregate supply determines real domestic
output by estimating the production function,
domestic investment, labour demand, and real
wages. Aggregate demand determines aggregate
household real consumption expenditure in the
economy while the price block estimates
producer and consumer prices.
The external segment identifies the major
components in the current account of the
balance of payment and the variation in the
level of exchange rate. It estimates the real
exports of goods and services, the real imports
of goods and services and the Rand/ US dollar
nominal exchange rate.
The model estimates the monetary supply
while assuming that the interest rate is
exogenously determined in the system. This is
done by following the principle that monetary
SAJEMS NS 15 (2012) No 2
authority directly controls interest rates.
Government revenue is estimated in the
model while government expenditure is assumed
to be exogenously determined by the political
leadership.
The important inter-linkages and feedbacks
of the various macroeconomic variables and
estimated equations in the system are revealed
in the model closure. The type of closure reveals the features of the model developed and
how the various policy simulations/ scenarios
would feed back into the entire system.
The production function (GDP) is estimated
by making the supply side of the economy
more active than the demand side. Therefore,
the price (producer and consumer) equations
serve as the link between the demand side and
the supply side of the economy through excess
demand and capacity utilisation. This is
presented as:
GDP = f ( L, K , T )
Excess Demand = GDE / GDP
GDE = C + I + G
Capacity Utilisation = GDP / GDP_POTENTIAL
where L is labour employment, K is capital
stock, T is technology, GDE is gross domestic
expenditure, C is household consumption
expenditure, I is domestic investment, G is
total government expenditure, Z is the imports
of goods & services, and GDP_POTENTIAL
is the potential level of GDP.
The potential level of output in the economy
is estimated by using the coefficients of labour
and capital from the production function with
the potential level of capital stock, labour
employment and total factor productivity.
These variables are generated using the
Hodrick-Prescott (HP) Filter technique. This is
a smoothing method that is widely used among
macroeconomists to obtain a smooth estimate
of the long-term trend component of a series
(Akanbi & Du Toit, 2011; Du Toit, 1999).
The long-run core structural equations
estimated from the four segments of the economy
are presented as follows:
The real segment
This sector consists of the aggregate supply,
the aggregate demand and the price block. The
aggregate supply determines the real domestic
output by estimating the production function,
the domestic investment, labour demand, and
157
real wages. The aggregate demand determines
the aggregate household real consumption
expenditure in the economy while the price
block estimates the producer and consumer
prices.
Production function:
The standard production function is estimated
for the South African economy and is presented
as:
+
+
Yt = f ( N t , K t )
(1)
where Yt is the Gross Domestic Product
(GDP), N t is the labour employment and K t
is the capital stock.
Domestic investment (real gross capital
formation):
This study considered the neoclassical approach
(Jorgenson, 1963) in estimating the domestic
investment function, since it incorporates all
cost minimizing and profit maximizing decision
making processes by firms. This approach has
also been adopted in Du Toit, 1999; Du Toit
and Moolman, 2004; Pretorius, 1998; and Akanbi
and Du Toit, 2011. The long-run domestic
investment function for South Africa is
modelled as a function of output, user cost of
capital, and capacity utilisation and is presented
below as:
+
−
+
I t = f (Yt , ucct , cu t )
(2)
where I t is the gross domestic investment, cu t
is the level of capacity utilisation, and ucct is
the user cost of capital.
Labour demand and real wage determination:
In modelling the labour market, the standard
labour-demand equation and a wage-adjustment
equation are defined and estimated. However,
the long-run labour-demand function is presented
as:
−
+
N t = f (rwt , Yt )
(3)
where rwt is the real wage rate.
The real wage equation follows Allen and
Nixon (1997:147) and is specified in this study
as:
+
rwt = f (labprodt , unempt )
(4)
where labprod t is the labour productivity and
unempt is unemployment.
Household real consumption expenditure:
The long-run household consumption is a
function of real disposable income, real
158
SAJEMS NS 15 (2012) No 2
wealth, and the real interest rate and this is
specified as:
+
+
+
hh _ rcon expt = f (hh _ dis _ inct , rwealtht , r int t ) (5)
where hh _ rcon expt is the household real consumption expenditure, hh _ dis _ inct is the
household real disposable income, rwealtht is
the real wealth (proxy by real domestic credit),
and r int t is the real rate of interest.
Consumer and producer prices:
The production price equation follows Layard
and Nickell (1986) and the long-run specification is presented
as:
+
+
+
+
+
p
Pt = f (wt , cu t , ucct , elect _ pt , petrol _ pt ) (6)
p
where wt is the nominal wage rate, Pt is the
production price index, petrol _ pt is pump petrol
prices and elect _ pt is the electricity prices.
Consumer prices which are directly related
to production+ prices
are also specified as:
+
+
+
Ctp = f ( Pt p , imptp , excessd t , excht )
(7)
p
t
where C t is the consumer price, imp is the
import price on consumption goods, excht is
the exchange rate and excessd t is the excess
demand.
p
The external segment
The external sector identifies the major components in the current account of the balance
of payments and the variation in the level of
exchange rate. It estimates the real exports of
goods and services, the real imports of goods
and services and the naira/ US dollar nominal
exchange rate.
Real exports of goods and services:
The demand for real exports of goods and
services in the long run is mainly driven by the
level of world income, the exchange rate and
the relative prices of goods and services. The
real exports function
is, however,
specified as:
_
+
+
r expt = f (wYt , relpt , excht )
(8)
where r exp t is the real exports of goods and
services, wYt is the real world (US) income,
relpt is the relative price of goods and services
(the ratio of domestic prices to US prices).
Real imports of goods and services:
The demand for real imports of goods and
services in the long run is mainly driven by the
level of domestic income, the exchange rate
and the relative prices of goods and services.
The real imports function is therefore specified
as:
+
+
−
rimpt = f (Yt , relpt , excht )
(9)
where rimpt is the real imports of goods and
services.
Nominal Exchange Rate:
The underlying theory for the specification of
the nominal exchange rate equation follows
Dornbusch (1976, 1980) and Frankel (1979).
The long-run nominal exchange rate is
specified as follows:
−
−
+
excht = f (relYt , rel int t , relpt )
(10)
where relYt is the relative income (the ratio of
domestic GDP to US GDP), and rel int t is the
relative interest rate (the ratio of domestic
interest rate to US interest rate).
Monetary segment
The model estimates the money supply while
assuming that interest rate is exogenously
determined in the system. This is done
following the principle that the monetary
authority directly controls interest.
Money supply:
The money-supply equation is assumed to be
an inverted interest-rate function. This is
derived as:
−
+
RMst = f (int t , Yt )
(11)
where RMst is the real monetary aggregate.
The government segment
In this study, the government sector is assumed
to be exogenously determined. Government
revenue is estimated as a function of GDP and
exchange rate, since about 95 per cent of
revenue comes from
taxes.
This is derived as:
+
+
govtrevt = f (excht , Yt )
(12)
where govtrevt is total government revenue.
The summary of the entire model is
presented in the form of the flow chart in
Figure 1.1. The chart highlights the major
contemporaneous feedback processes of the
interactions between the segments investigated
in the model.
SAJEMS NS 15 (2012) No 2
159
Figure 1.1
A flow chart of the model
Real sector
Supply
Demand
Prices
Monetary
sector
cuExternal
gde
sector
Public
sector
Source: Akanbi & Du Toit, (2011)
As shown in the flow chart above, the price
block serves as a major linkage between the
supply side and aggregate demand side through
capacity utilisation and excess demand.
Changes in these variables cause fluctuations
in price, which affect production and demand
and also cause changes in the other sectors of
the economy. The monetary, external and
public segments are linked directly to the
supply side and demand side of the economy
through changes in the interest rate, government spending and exchange rate. The
institutional characteristics of the economy,
with its associated policy behaviour, are
incorporated through the public and monetary
segment, whereas the interaction with the rest
of the world is captured through the external
segment.
In view of the above discussion, the
production function and other behavioural
equations are estimated using Engle and
Granger (1987) techniques. This procedure is
widely accepted in the macro-econometric
literature as it avoids the common problem of
spurious regressions that gives an incorrect
impression of an existing long-run relationship
between two or more variables. As laid out
in Enders (2004:335), Engle and Granger’s
proposed four-step procedure is followed.
Given the disaggregated sectoral model adopted
in the study, all the behavioural equations were
estimated for each of the eight (8) sectors
except for cases where disaggregation was not
possible (i.e. money supply, exchange rate,
government revenue, CPI, PPI, and household
consumption expenditure).
All the data used in the study were obtained
from the South African Reserve Bank (SARB),
IFS (International Financial Statistics), World
Bank database: African Development Indicators
and World Development Indicators, and Quantec
database. Annual data series, which cover the
period 1970-2009, were used to estimate the
parameters of the model and where appropriate
the variables were transformed into real figures
using the GDP deflator (2005=base year).
The following graphs depict the long-run
path of an increased Eskom capital expenditure
on major macro variables in the economy.
Figures 1.1-10 depict the long-run path of
capital expenditure increases. As mentioned
earlier, the first value represents the short-run
impact while the last value represents the longrun impact. All the results are reported as
percentage changes (elasticity) from the
baseline scenario.
160
SAJEMS NS 15 (2012) No 2
Figure 1.2
Economy-wide 18 per cent over a 6-year period
Consumer Infl ati on
Employment
Exchange Rate (R/$)
. 15
. 025
. 08
. 10
. 020
. 04
. 05
. 015
. 00
. 010
-.05
. 005
-.10
. 000
. 00
-.04
-.08
-.15
-.12
-.16
-.005
75
80
85
90
95
00
05
-.20
75
80
85
Exports
90
95
00
05
75
80
GDP
. 02
. 01
. 10
. 12
. 08
. 10
90
95
00
05
. 08
. 06
. 00
85
Househol d Consumpti on
. 06
. 04
. 04
-.01
. 02
-.02
. 02
. 00
-.03
. 00
-.02
75
80
85
90
95
00
05
-.02
75
80
85
Imports
90
95
00
05
75
80
85
Investment
. 10
2.0
. 08
1.5
. 06
1.0
. 04
0.5
. 02
0.0
90
95
00
05
00
05
Real Wages
. 06
. 05
. 04
. 03
. 00
. 02
. 01
-0. 5
75
80
85
90
95
00
05
. 00
75
80
85
90
95
00
05
75
80
85
90
95
Figure 1.3
Manufacturing sector 18 per cent over a 6-year period
Em ploym ent
Exports
.020
.02
.016
.01
.012
.00
.008
-.01
.004
-.02
.000
-.03
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
1990
1995
2000
2005
2000
2005
2000
2005
Im ports
.08
.10
.08
.06
.06
.04
.04
.02
.00
.02
-.02
.00
-.04
1975
1980
1985
1990
1995
2000
2005
1975
1980
Investm ent
1985
1990
1995
Real W ages
.12
.08
.08
.06
.04
.04
.00
.02
-.04
.00
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
SAJEMS NS 15 (2012) No 2
161
Figure 1.4
Mining sector 18 per cent over a 6-year period
Em ploym ent
Exports
.10
.04
.05
.02
.00
.00
-.05
-.02
-.10
-.04
-.15
-.06
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
1990
1995
2000
2005
2000
2005
2000
2005
2000
2005
2000
2005
2000
2005
Im ports
.15
.12
.10
.08
.05
.04
.00
.00
-.05
-.04
-.10
-.08
-.15
-.12
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
Investm ent
1990
1995
Real W ages
.12
.3
.2
.08
.1
.04
.0
.00
-.1
-.04
-.2
-.08
-.3
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
Figure 1.5
Agricultural sector 18 per cent over a 6-year period
Em ploym ent
Exports
.03
.06
.02
.04
.01
.02
.00
.00
-.01
-.02
-.02
-.04
-.03
-.06
-.04
-.08
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
1990
1995
Im ports
.08
.3
.2
.04
.1
.00
.0
-.04
-.1
-.08
-.2
-.12
-.3
1975
1980
1985
1990
1995
2000
2005
1975
1980
Investm ent
1985
1990
1995
Real W ages
.4
.08
.2
.04
.0
.00
-.2
-.04
-.4
-.6
-.08
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
162
SAJEMS NS 15 (2012) No 2
Figure 1.6
Construction sector 18 per cent over a 6-year period
Employment
Exports
.003
.10
.002
.05
.001
.00
.000
-.05
-.001
-.10
-.002
-.003
-.15
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
1990
1995
2000
2005
2000
2005
2000
2005
Imports
.012
.3
.008
.2
.004
.1
.000
.0
-.004
-.1
-.008
-.2
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
Investment
1990
1995
Real Wages
.04
.008
.03
.006
.02
.004
.01
.002
.00
.000
-.01
-.002
-.02
-.03
-.004
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
Figure 1.7
Electricity, gas & water sector 18 per cent over a 6-year period
Em ploym ent
Exports
3.0
.8
2.5
.6
2.0
.4
1.5
.2
1.0
.0
0.5
-.2
0.0
-0.5
-.4
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
8
3
6
2
4
1
2
0
0
-1
-2
1980
1985
1990
1995
2000
2005
2000
2005
Real W ages
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1975
1980
1985
1990
1995
1995
2000
2005
2000
2005
Im ports
4
1975
1990
1975
1980
1985
1990
1995
SAJEMS NS 15 (2012) No 2
163
Figure 1.8
Wholesale & retail trade sector 18 per cent over a 6-year period
Em ploym ent
Exports
.030
.04
.025
.02
.020
.00
.015
-.02
.010
-.04
.005
.000
-.06
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
1990
1995
2000
2005
2000
2005
2000
2005
2000
2005
2000
2005
2000
2005
Im ports
.08
.040
.035
.06
.030
.04
.025
.02
.020
.015
.00
.010
-.02
.005
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
Inves tm ent
1990
1995
Real W ages
.3
.08
.06
.2
.04
.1
.02
.0
.00
-.1
-.02
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
Figure 1.9
Transport and communication sector 18 per cent over a 6-year period
Em ploym ent
Exports
.20
.03
.15
.02
.01
.10
.00
.05
-.01
.00
-.02
-.05
-.03
-.10
-.04
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
GDP
1990
1995
Im ports
.08
.05
.04
.06
.03
.04
.02
.02
.01
.00
.00
-.01
-.02
-.02
1975
1980
1985
1990
1995
2000
2005
1975
1980
Investm ent
1985
1990
1995
Real W ages
.16
.25
.20
.12
.15
.08
.10
.05
.04
.00
.00
-.05
-.04
-.10
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
164
SAJEMS NS 15 (2012) No 2
Figure 1.10
Financial sector 18 per cent over a 6 year period
Em ploym ent
Exports
.002
.10
.000
.05
-.002
-.004
.00
-.006
-.05
-.008
-.010
-.10
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
GDP
1995
2000
2005
2000
2005
2000
2005
Im ports
.030
.08
.025
.04
.020
.015
.00
.010
.005
-.04
.000
-.005
-.08
1975
1980
1985
1990
1995
2000
2005
1975
1980
Inves tm ent
1985
1990
1995
Real W ages
.06
.04
.03
.04
.02
.02
.01
.00
.00
-.02
-.01
1975
1980
1985
1990
1995
2000
2005
1975
1980
1985
1990
1995
Appendix 2: Actual and fitted values
(model simulations)
Figure 2.1: GDP (Rmillion)
Figure 2.2: Gross fixed investment (Rmillion)
2,000,000
400,000
1,800,000
350,000
1,600,000
300,000
1,400,000
1,200,000
250,000
1,000,000
200,000
800,000
150,000
600,000
400,000
1970
1975
1980
1985
1990
Actual
1995
Fitted
2000
2005
100,000
1970
1975
1980
1985
1990
Actual
1995
Fitted
2000
2005
SAJEMS NS 15 (2012) No 2
165
Figure 2.3: Employment (unit)
Figure 2.4: Real wages (Rmillon)
14,000,000
900,000
12,000,000
800,000
10,000,000
700,000
8,000,000
600,000
6,000,000
500,000
4,000,000
400,000
2,000,000
1970
1975
1980
1985
1990
Actual
1995
2000
2005
300,000
1970
1975
1980
1985
Fitted
1990
Actual
Figure 2.5: Household expenditure (Rmillion)
1,400,000
1995
2000
2005
Fitted
Figure 2.6: Consumer price index
140
120
1,200,000
100
1,000,000
80
800,000
60
600,000
40
400,000
20
200,000
1970
1975
1980
1985
1990
Actual
1995
2000
2005
0
1970
1975
1980
Fitted
1985
1990
Actual
Figure 2.7: Producer price index
1995
2000
2005
Fitted
Figure 2.8: Exports (Rmillon)
140
550,000
120
500,000
450,000
100
400,000
80
350,000
60
300,000
40
250,000
20
0
1970
200,000
1975
1980
1985
1990
Actual
1995
Fitted
2000
2005
150,000
1970
1975
1980
1985
1990
Actual
1995
Fitted
2000
2005
166
SAJEMS NS 15 (2012) No 2
Figure 2.9: Imports (Rmillion)
Figure 2.10: Exchange rate (R/US$)
600,000
12
10
500,000
8
400,000
6
300,000
4
200,000
100,000
1970
2
1975
1980
1985
1990
1995
Actual
2000
2005
0
1970
1975
1980
1985
Fitted
1990
Actual
Figure 2.11: Money supply (Rmillion)
1995
2000
2005
Fitted
Figure 2.12: Government revenue (Rmillon)
1,600,000
480,000
440,000
1,400,000
400,000
1,200,000
360,000
320,000
1,000,000
280,000
800,000
240,000
200,000
600,000
160,000
400,000
200,000
1970
120,000
1975
1980
1985
1990
Actual
1995
2000
2005
80,000
1970
1975
1980
1985
Fitted
1990
Actual
Detailed results
Table 3.2
Short-run output changes by sub-industry
18 per cent
Irrigated field
0.09
Dry field
0.09
Irrigated horticulture
0.11
Dry horticulture
0.11
Livestock
0.2
Forestry
0.21
Other agriculture
0.21
Coal
0.75
Gold
1.34
Crude, petroleum and gas
0.1
Other mining
0.18
1995
Fitted
2000
2005
SAJEMS NS 15 (2012) No 2
167
Food
0.2
Textiles
0.23
Footwear
0.27
Chemicals and rubber
0.23
Petroleum refineries
0.14
Other non-metallic mineral products
0.28
Iron and steel
2.6
Non-ferrous metal
0.96
Other metal products
0.36
Other machinery
0.33
Electrical machinery
0.37
Radio
0.41
Transport equipment
0.36
Wood, paper and pulp
0.26
Other manufacturing
0.1
Electricity
5.65
Water
0.4
Construction
0.12
Trade
0.29
Hotels
0.18
Transport services
0.34
Communication services
0.25
Financial institutions
0.15
Real estate
0.06
Business activities
0.29
General government
0.45
Health services
0.41
Other service activities
0.38
Table 3.3
Short-run employment changes by sub-industry
18 per cent
Irrigated field
0.48
Dry field
0.47
Irrigated horticulture
0.35
Dry horticulture
0.35
Livestock
0.52
Forestry
0.4
Other agriculture
0.6
Coal
1.82
Gold
2.04
Crude, petroleum and gas
0.18
Other mining
0.38
Food
0.46
Textiles
0.27
Footwear
0.43
Chemicals and rubber
0.41
Petroleum refineries
0.42
Other non-metallic mineral products
0.54
168
SAJEMS NS 15 (2012) No 2
Iron and steel
5.31
Non-ferrous metal
3.5
Other metal products
0.54
Other machinery
0.49
Electrical machinery
0.72
Radio
0.51
Transport equipment
0.51
Wood, paper and pulp
0.52
Other manufacturing
0.2
Water
2.01
Construction
-0.1
Trade
0.51
Hotels
0.51
Transport services
0.7
Communication services
0.53
Financial institutions
0.38
Real estate
0.38
Business activities
0.33
General government
0.5
Health services
0.83
Other service activities
0.57
Table 3.4
Long-run output changes by sub-industry
18 per cent
Irrigated field
0.32
Dry field
0.31
Irrigated horticulture
0.19
Dry horticulture
0.19
Livestock
0.75
Forestry
0.46
Other agriculture
0.65
Coal
1.13
Gold
1.82
Crude, petroleum and gas
0.46
Other mining
0
Food
0.66
Textiles
0.14
Footwear
0.59
Chemicals and rubber
0.38
Petroleum refineries
0.61
Other non-metallic mineral products
0.25
Iron and steel
4.07
Non-ferrous metal
3.51
Other metal products
0.34
Other machinery
0.22
Electrical machinery
0.35
Radio
0.72
Transport equipment
0.3
SAJEMS NS 15 (2012) No 2
Wood, paper and pulp
Other manufacturing
169
0.34
-0.25
Water
0.87
Construction
0.15
Trade
0.59
Hotels
0.89
Transport services
0.82
Communication services
1.11
Financial institutions
0.91
Real estate
0.96
Business activities
0.56
Health services
1.42
Other service activities
0.62
Table 3.5
Long-run industry unskilled employment impacts by sub-industry
18 per cent
Irrigated field
0.14
Dry field
0.12
Irrigated horticulture
0.01
Dry horticulture
0.01
Livestock
0.72
Forestry
0.39
Other agriculture
0.59
Coal
1.09
Gold
2.04
Crude, petroleum and gas
Other mining
0.43
-0.12
Food
0.55
Textiles
0.1
Footwear
0.37
Chemicals and rubber
0.3
Petroleum refineries
0.5
Other non-metallic mineral products
0.06
Iron and steel
3.78
Non-ferrous metal
3.15
Other metal products
0.15
Other machinery
0.05
Electrical machinery
0.08
Radio
0.57
Transport equipment
0.18
Wood, paper and pulp
0.18
Other manufacturing
-0.5
Electricity
-7.35
Water
Construction
0.34
-0.3
Trade
0.37
Hotels
0.53
Transport services
0.65
170
SAJEMS NS 15 (2012) No 2
Communication services
0.91
Financial institutions
0.69
Real estate
0.6
Business activities
General government
0.37
-0.1
Health services
0.8
Other service activities
0.29